Diagnosis for Rotating Machinery using Modelica

Abstract

Diagnosing the complex system accurately based on stochastic method requires an enormous amount of data, both with and without faults. Acquiring operation data with all kinds of faults for each components is very hard and costly. To generate data for rotating machinery diagnosis, we have developed rotating machinery library using Modelica. It provides the basic components such as rotor, shaft, bearing, coupling, housing and support. Its component models are implemented on the basis of the transfer matrix method in rotor dynamics. To validate our models, we compared both Modelica simulation and experiment with a rotor kit as a test case.